Wolfram 2

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Captions on this page:
Examples of three-color totalistic rules that yield patterns with seemingly random features. Three hundred steps of
evolution are shown in each case.

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In detail, some of the patterns are definitely more complicated than those seen in elementary rules.
But at the level of overall behavior, there are no fundamental differences. And in the case of nested
patterns even the specific structures seen are usually the same as for elementary rules. Thus, for
example, the structure in codes 237 and 948 is the most common, followed by the one in code 1749.
The only new structure not already seen in elementary rules is the one in code 420--but this occurs
only quite rarely.
About 85% of all three-color totalistic cellular automata produce behavior that is ultimately quite
regular. But just as in elementary cellular automata, there are some rules that yield behavior that
seems in many respects random. A few examples of this are given on the facing page.
Beyond fairly uniform random behavior, there are also cases similar to elementary rule 110 in
which definite structures are produced that interact in complicated ways. The next page gives a few
examples. In the first case shown, the pattern becomes repetitive after about 150 steps. In the other
two cases, however, it is much less clear what will ultimately happen. The following pages [67, 68]
continue these patterns for 3000 steps. But even after this many steps it is still quite unclear what the
final behavior will be.
Looking at pictures like these, it is at first difficult to believe that they can be generated just by
following very simple underlying cellular automaton rules. And indeed, even if one accepts this, there
is still a tendency to assume that somehow what one sees must be a consequence of some very special
feature of cellular automata.
As it turns out, complexity is particularly widespread in cellular automata, and for this reason it is
fortunate that cellular automata were the very first systems that I originally decided to study.
But as we will see in the remainder of this chapter, the fundamental phenomena that we discovered
in the previous chapter are in no way restricted to cellular automata. And although cellular automata
remain some of the very best examples, we will see that a vast range of utterly different systems all in
the end turn out to exhibit extremely similar types of behavior.

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Examples of three-color totalistic rules with highly complex behavior showing a mixture of regularity and
irregularity. The partitioning into identifiable structures is similar to what we saw in rule 110 on page 32.

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The pictures below show totalistic cellular automata whose overall patterns of growth seem, at
least at first, quite complicated. But it turns out that after only about 100 steps, three out of four of
these patterns have resolved into simple forms.
The one remaining pattern is, however, much more complicated. As shown on the next page, for
several thousand steps it simply grows, albeit somewhat irregularly. But then its growth becomes
slower. And inside the pattern parts begin to die out. Yet there continue to be occasional bursts of
growth. But finally, after a total of 8282 steps, the pattern resolves into 31 simple repetitive structures.

Examples of rules that yield patterns which seem to be on the edge between growth and extinction. For all but
code 1599, the fate of these patterns in fact becomes clear after less than 100 steps. A total of 250 steps are
shown here.

Three thousand steps in the evolution of the last two cellular automata from page 66. Despite the simplicity of their
underlying rules, the final patterns produced show immense complexity. In neither case is it clear what the final
outcome will be--whether apparent randomness will take over, or whether a simple repetitive form will emerge.

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Nine thousand steps in the evolution of the three-color totalistic cellular automaton with code number 1599.
Starting from a single gray cell, each column corresponds to 3000 steps. The outcome of the evolution finally
becomes clear after 8282 steps, when the pattern resolves into 31 simple repetitive structures.

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Mobile Automata

One of the basic features of a cellular automaton is that the colors of all the cells it contains are
updated in parallel at every step in its evolution.
But how important is this feature in determining the overall behavior that occurs? To address this
question, I consider in this section a class of systems that I call "mobile automata".
Mobile automata are similar to cellular automata except that instead of updating all cells in
parallel, they have just a single "active cell" that gets updated at each step--and then they have rules
that specify how this active cell should move from one step to the next.
The picture below shows an example of a mobile automaton. The active cell is indicated by a black
dot. The rule applies only to this active cell. It looks at the color of the active cell and its immediate
neighbors, then specifies what the new color of the active cell should be, and whether the active cell
should move left or right.
Much as for cellular automata, one can enumerate all possible rules of this kind; it turns out that
there are 65,536 of them. The pictures at the top of the next page show typical behavior obtained with
such rules. In cases (a) and (b), the active cell remains localized to a small region, and the behavior is
very simple and repetitive. Cases (c) through (f) are similar,

An example of a mobile automaton. Like a cellular automaton, a mobile automaton consists of a line of cells, with
each cell having two possible colors. But unlike a cellular automaton, a mobile automaton has only one "active cell"
(indicated here by a black dot) at any particular step. The rule for the mobile automaton specifies both how the
color of this active cell should be updated, and whether it should move to the left or right. The result of evolution for
a larger number of steps with the particular rule shown here is given as example (f) on the next page.

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except that the whole pattern shifts systematically to the right, and in cases (e) and (f) a sequence of
stripes is left behind.
But with a total of 218 out of the 65,536 possible rules, one gets somewhat different behavior, as
cases (g) and (h) above show. The active cell in these cases does not move in a strictly repetitive way,
but instead sweeps backwards and forwards, going progressively further every time.
The overall pattern produced is still quite simple, however. And indeed in the compressed form
below, it is purely repetitive.

Examples of mobile automata with various rules. In cases (a) through (f) the motion of the active cell is purely
repetitive. In cases (g) and (h) it is not. The width of the pattern in these cases after t steps grows roughly like
Sqrt[2t].

Compressed versions of the evolution of mobile automata (g) and (h) above, obtained by showing only those steps
at which the active cell is further to the left or right than it has ever been before.

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Of the 65,536 possible mobile automata with rules of the kind discussed so far it turns out that not
a single one shows more complex behavior. So can such behavior then ever occur in mobile automata?

One can extend the set of rules one considers by allowing not only the color of the active cell itself
but also the colors of its immediate neighbors to be updated at each step. And with this extension,
there are a total of 4,294,967,296 possible rules.
If one samples these rules at random, one finds that more than 99% of them just yield simple
repetitive behavior. But once in every few thousand rules, one sees behavior of the kind shown
below--that is not purely repetitive, but instead has a kind of nested structure.

A mobile automaton with slightly more complicated rules that yields a nested pattern. Each column on the left
shows 200 steps in the mobile automaton evolution. The compressed form of the pattern is based on a total of
8000 steps.

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The overall pattern is nevertheless still very regular. But after searching through perhaps 50,000
rules, one finally comes across a rule of the kind shown below--in which the compressed pattern
exhibits very much the same kind of apparent randomness that we saw in cellular automata like rule
30.
But even though the final pattern left behind by the active cell in the picture above seems in many
respects random, the motion of the active cell itself is still quite regular. So are there mobile automata
in which the motion of the active cell is also seemingly random? At first, I believed that there might
not be. But after searching through a few million rules, I finally found the example shown on the
facing page.

A mobile automaton that yields a pattern with seemingly random features. The motion of the active cell is still quite
regular, as the picture on the right shows. But when viewed in compressed form, as below, the overall pattern of
colors seems in many respects random. Each column on the right shows 200 steps of evolution; the compressed
form below corresponds to 50,000 steps.

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A mobile automaton in which the position of the active cell moves in a seemingly random way. Each column above
shows 400 steps; the compressed form corresponds to 50,000 steps. It took searching through a few million
mobile automata to find one with behavior as complex as what we see here.

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Despite the fact that mobile automata update only one cell at a time, it is thus still possible for
them to produce behavior of great complexity. But while we found that such behavior is quite

common in cellular automata, what we have seen in this section indicates that it is rather rare in
mobile automata.
One can get some insight into the origin of this difference by studying a class of generalized
mobile automata, that in a sense interpolate between ordinary mobile automata and cellular automata.
The basic idea of such generalized mobile automata is to allow more than one cell to be active at a
time. And the underlying rule is then typically set up so that under certain circumstances an active cell
can split in two, or can disappear entirely.
Thus in the picture below, for example, new active cells end up being created every few steps.
If one chooses generalized mobile automata at random, most of them will produce simple
behavior, as shown in the first few pictures on the facing page. But in a few percent of all cases, the
behavior is much more complicated. Often the arrangement of active cells is still quite regular,
although sometimes it is not.
But looking at many examples, a certain theme emerges: complex behavior almost never occurs
except when large numbers of cells are active at the same time. Indeed there is, it seems, a significant
correlation between overall activity and the likelihood of complex behavior. And this is part of why
complex behavior is so much more common in cellular automata than in mobile automata.

A generalized mobile automaton in which any number of cells can be active at a time. The rule given above is
applied to every cell that is active at a particular step. In many cases, the rule specifies just that the active cell
should move to the left or right. But in some cases, it specifies that the active cell should split in two, thereby
creating an additional active cell.

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Examples of generalized mobile automata with various rules. In case (a), only a limited number of cells ever
become active. But in all the other cases shown active cells proliferate forever. In case (d), almost all cells are
active, and the system operates essentially like a cellular automaton. In the remaining cases somewhat
complicated patterns of cells are active. Note that unlike in ordinary mobile automata, examples of complex
behavior like those shown here are comparatively easy to find.

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Turing Machines

In the history of computing, the first widely understood theoretical computer programs ever
constructed were based on a class of systems now called Turing machines.
Turing machines are similar to mobile automata in that they consist of a line of cells, known as the
"tape", together with a single active cell, known as the "head". But unlike in a mobile automaton, the

head in a Turing machine can have several possible states, represented by several possible arrow
directions in the picture below.
And in addition, the rule for a Turing machine can depend on the state of the head, and on the
color of the cell at the position of the head, but not on the colors of any neighboring cells.
Turing machines are still widely used in theoretical computer science. But in almost all cases, one
imagines constructing examples to perform particular tasks, with a huge number of possible states and
a huge number of possible colors for each cell.
But in fact there are non-trivial Turing machines that have just two possible states and two possible
colors for each cell. The pictures on the facing page show examples of some of the 4096 machines of
this kind. Both repetitive and nested behavior are seen to occur, though nothing more complicated is
found.

An example of a Turing machine. Like a mobile automaton, the Turing machine has one active cell or "head", but
now the head has several possible states, indicated by the directions of the arrows in this picture.

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From our experience with mobile automata, however, we expect that there should be Turing
machines that have more complex behavior.
With three states for the head, there are about three million possible Turing machines. But while
some of these give behavior that looks slightly more complicated in detail, as in cases (a) and (b) on
the next page, all ultimately turn out to yield just repetitive or nested patterns--at least if they are
started with all cells white.
With four states, however, more complicated behavior immediately becomes possible. Indeed, in
about five out of every million rules of this kind, one gets patterns with features that seem in many
respects random, as in the pictures on the next two pages [80, 81].
So what happens if one allows more than four states for the head? It turns out that there is almost
no change in the kind of behavior one sees. Apparent randomness becomes slightly more common, but
otherwise the results are essentially the same.
Once again, it seems that there is a threshold for complex behavior--that is reached as soon as one
has at least four states. And just as in cellular automata, adding more complexity to the underlying
rules does not yield behavior that is ultimately any more complex.

Examples of Turing machines with two possible states for the head. There are a total of 4096 rules of this kind.
Repetitive and nested patterns are seen, but nothing more complicated ever occurs.

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Examples of Turing machines with three and four possible states. With three possible states, only repetitive and
nested patterns are ever ultimately produced, at least starting with all cells white. But with four states, more
complicated patterns are generated. The top set of pictures show the first 150 steps of evolution according to
various different rules, starting with the head in the first state (arrow pointing up), and all cells white. The bottom
set of pictures show the evolution in each case in a compressed form. Each of these pictures includes the first 50
steps at which the head is further to the left or right than it has ever been before.

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A Turing machine that exhibits behavior which seems in many respects random. The Turing machine has four
possible states for its head, and two possible colors for each cell on its tape. It starts with all cells white,
corresponding to a blank tape. Each column above shows 250 steps of evolution; the compressed form on the left
corresponds to a total of 20,000 steps

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Substitution Systems

One of the features that cellular automata, mobile automata and Turing machines all have in
common is that at the lowest level they consist of a fixed array of cells. And this means that while the
colors of these cells can be updated according to a wide range of different possible rules, the
underlying number and organization of cells always stays the same.
Substitution systems, however, are set up so that the number of elements can change. In the typical
case illustrated below, one has a sequence of elements--each colored say black or white--and at each
step each one of these elements is replaced by a new block of elements.
In the simple cases shown, the rules specify that each element of a particular color should be
replaced by a fixed block of new elements, independent of the colors of any neighboring elements.
And with these kinds of rules, the total number of elements typically grows very rapidly, so that
pictures like those above quickly become rather unwieldy. But at least for these kinds of rules, one can
make clearer pictures by thinking of each step not as replacing every element by a sequence of
elements that are drawn the same size, but rather of subdividing each element into several that are
drawn smaller.
In the cases on the facing page, I start from a single element represented by a long box going all
the way across the picture. Then on successive steps the rules for the substitution system specify how
each box should be subdivided into a sequence of shorter and shorter boxes.


Examples of substitution systems with two possible kinds of elements, in which at every step each kind of element
is replaced by a fixed block of new elements. In the first case shown, the total number of elements obtained
doubles at every step; in the second case, it follows a Fibonacci sequence, and increases by a factor of roughly
(1+Sqrt[5])/2 1.618 at every step. The two substitution systems shown here correspond to the second and
third examples in the pictures on the following two pages [83, 84].

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The pictures at the top of the next page show a few more examples. And what we see is that in all
cases there is obvious regularity in the patterns produced. Indeed, if one looks carefully, one can see
that every pattern just consists of a collection of identical nested pieces.
And ultimately this is not surprising. After all, the basic rules for these substitution systems specify
that any time an element of a particular color appears it will always get subdivided in the same way.
The nested structure becomes even clearer if one represents elements not as boxes, but instead as
branches on a tree. And with this setup the idea is to start from the trunk of the tree, and then at each
step to use the rules for the substitution system to determine how every branch should be split into
smaller branches.

Examples of substitution systems in which every element is drawn as being subdivided into a sequence of new
elements at each step. In all cases the overall patterns obtained can be seen to have a very regular nested form.
Rule (b) gives the so-called Thue-Morse sequence, which we will encounter many times in this book. Rule (c) is
related to the Fibonacci sequence. Rule (d) gives a version of the Cantor set.

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Then the point is that because the rules depend only on the color of a particular branch, and not on
the colors of any neighboring branches, the subtrees that are generated from all the branches of the
same color must have exactly the same structure, as in the pictures below.

More examples of neighbor-independent substitution systems like those on the previous page. Each rule yields a
different sequence of elements, but all of them ultimately have simple nested forms.

The evolution of the same substitution systems as on the previous page, but now shown in terms of trees. Starting
from the trunk at the bottom, the rules specify that at each step every branch of a particular color should split into
smaller branches in the same way. The result is that each tree consists of a collection of progressively smaller
subtrees with the same structure. On page 400 I will use similar systems to discuss the growth of actual trees and
leaves

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To get behavior that is more complicated than simple nesting, it follows therefore that one must
consider substitution systems whose rules depend not only on the color of a single element, but also
on the color of at least one of its neighbors. The pictures below show examples in which the rules for
replacing an element depend not only on its own color, but also on the color of the element
immediately to its right.

In the first example, the pattern obtained still has a simple nested structure. But in the second
example, the behavior is more complicated, and there is no obvious nested structure.
One feature of both examples, however, is that the total number of elements never decreases from
one step to the next. The reason for this is that the basic rules we used specify that every single
element should be replaced by at least one new element.

Examples of substitution systems whose rules depend not just on the color of an element itself, but also on the
color of the element immediately to its right. Rules of this kind cannot readily be interpreted in terms of simple
subdivision of one element into several. And as a result, there is no obvious way to choose what size of box should
be used to represent each element in the picture. What I do here is simply to divide the whole width of the picture
equally among all elements that appear at each step. Note that on every step the rightmost element is always
dropped, since no rule is given for how to replace it.

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It is, however, also possible to consider substitution systems in which elements can simply
disappear. If the rate of such disappearances is too large, then almost any pattern will quickly die out.
And if there are too few disappearances, then most patterns will grow very rapidly.
But there is always a small fraction of rules in which the creation and destruction of elements is
almost perfectly balanced.
The picture above shows one example. The number of elements does end up increasing in this
particular example, but only by a fixed amount at each step. And with such slow growth, we can again
represent each element by a box of the same size, just as in our original pictures of substitution
systems on page 82.
When viewed in this way, however, the pattern produced by the substitution system shown above
is seen to have a simple repetitive form. And as it turns out, among substitution systems with the same
type of rules, all those which yield slow growth also seem to produce only such simple repetitive
patterns.
Knowing this, we might conclude that somehow substitution systems just cannot produce the kind
of complexity that we have seen in systems like cellular automata. But as with mobile automata and
with Turing machines, we would again be wrong. Indeed, as the pictures on the facing page
demonstrate, allowing elements to have three or four colors rather than just two immediately makes
much more complicated behavior possible.

Two views of a substitution system whose rules allow both creation and destruction of elements. In the view on the
left, the boxes representing each element are scaled to keep the total width the same, whereas on the right each
box has a fixed size, as in our original pictures of substitution systems on page 82. The right-hand view shows that
the rates of creation and destruction of elements are balanced closely enough that the total number of elements
grows by only a fixed amount at each step.

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Examples of substitution systems that have three and four possible colors for each element. The particular rules
shown are ones that lead to slow growth in the total number of elements. Note that on each line in each picture,
only the order of elements is ever significant: as the insets show, a particular element may change its position as a
result of the addition or subtraction of elements to its left. Note that the pattern in case (a) does eventually repeat,
while the one in case (b) eventually shows a nested structure.

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As it turns out, the first substitution system shown works almost exactly like a cellular automaton.
Indeed, away from the right-hand edge, all the elements effectively behave as if they were lying on a
regular grid, with the color of each element depending only on the previous color of that element and
the element immediately to its right.
The second substitution system shown again has patches that exhibit a regular grid structure. But
between these patches, there are regions in which elements are created and destroyed. And in the other
substitution systems shown, elements are created and destroyed throughout, leaving no trace of any
simple grid structure. So in the end the patterns we obtain can look just as random as what we have
seen in systems like cellular automata.

Sequential Substitution Systems

None of the systems we have discussed so far in this chapter might at first seem much like
computer programs of the kind we typically use in practice. But it turns out that there are for example
variants of substitution systems that work essentially just like standard text editors.
The first step in understanding this correspondence is to think of substitution systems as operating
not on sequences of colored elements but rather on strings of elements or letters. Thus for example the
state of a substitution system at a particular step can be represented by the string "ABBBABA", where
the "A"'s correspond to white elements and the "B"'s to black ones.
The substitution systems that we discussed in the previous section work by replacing each element
in such a string by a new sequence of elements--so that in a sense these systems operate in parallel on
all the elements that exist in the string at each step.
But it is also possible to consider sequential substitution systems, in which the idea is instead to
scan the string from left to right, looking for a particular sequence of elements, and then to perform a
replacement for the first such sequence that is found. And this setup is now directly analogous to the
search-and-replace function of a typical text editor.

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The picture below shows an example of a sequential substitution system in which the rule specifies
simply that the first sequence of the form "BA" found at each step should be replaced with the
sequence "ABA".

The behavior in this case is very simple, with longer and longer strings of the same form being
produced at each step. But one can get more complicated behavior if one uses rules that involve more
than just one possible replacement. The idea in this case is at each step to scan the string repeatedly,
trying successive replacements on successive scans, and stopping as soon as a replacement that can be
used is found.
The picture on the next page shows a sequential substitution system with rule {"ABA"->"AAB",
"A"->"ABA"} involving two possible replacements. Since the sequence "ABA" occurs in the initial
string that is given, the first replacement is used on the first step. But the string "BAAB" that is
produced at the second step does not contain "ABA", so now the first replacement cannot be used.
Nevertheless, since the string does contain the single element "A", the second replacement can still be
used.
Despite such alternation between different replacements, however, the final pattern that emerges is
very regular. Indeed, if one allows only two possible replacements--and two possible elements--

An example of a very simple sequential substitution system. The light squares can be thought of as corresponding
to the element A, and the dark squares to the element B. At each step, the rule then specifies that the string which
exists at that step should be scanned from left to right, and the first sequence BA that is found should be replaced
by ABA. In the picture, the black dots indicate which elements are being replaced at each step. In the case shown,
the initial string is BABA. At each step, the rule then has the effect of adding an A inside the string.

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then it seems that no rule ever gives behavior that is much more complicated than in the picture above.
And from this one might be led to conclude that sequential substitution systems could never
produce behavior of any substantial complexity. But having now seen complexity in many other kinds
of systems, one might suspect that it should also be possible in sequential substitution systems.
And it turns out that if one allows more than two possible replacements then one can indeed
immediately get more complex behavior. The pictures on the facing page show a few examples. In
many cases, fairly regular repetitive or nested patterns are still produced.
But about once in every 10,000 randomly selected rules, rather different behavior is obtained.
Indeed, as the picture on the following page demonstrates, patterns can be produced that seem in many
respects random, much like patterns we have seen in cellular automata and other systems.
So this leads to the rather remarkable conclusion that just by using the simple operations available
even in a very basic text editor, it is still ultimately possible to produce behavior of great complexity.

A sequential substitution system whose rule involves two possible replacements. At each step, the whole string is
scanned once to try to apply the first replacement, and is then scanned again if necessary to try to apply the
second replacement.

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Examples of sequential substitution systems whose rules involve three possible replacements. In all cases, the
systems are started from the initial string BAB. The black dots indicate the elements that are replaced at each step.

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An example of a sequential substitution system that yields apparently random behavior. Each column on the right-
hand side shows the evolution of the system for 250 steps. The compressed picture on the left is made by evolving
for a million steps, but showing only steps at which the string becomes longer than it has ever been before. (The
rule is the same as (g) on the previous page.)

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Tag Systems

One of the goals of this chapter is to find out just how simple the underlying structure of a system
can be while the system as a whole is still capable of producing complex behavior. And as one
example of a class of systems with a particularly simple underlying structure, I consider here what are
sometimes known as tag systems.
A tag system consists of a sequence of elements, each colored say black or white. The rules for the
system specify that at each step a fixed number of elements should be removed from the beginning of
the sequence. And then, depending on the colors of these elements, one of several possible blocks is
tagged onto the end of the sequence.
The pictures below show examples of tag systems in which just one element is removed at each
step. And already in these systems one sometimes sees behavior that looks somewhat complicated.
But in fact it turns out that if only one element is removed at each step, then a tag system always
effectively acts just like a slow version of a neighbor-independent substitution system of the kind we
discussed on page 83. And as a result, the pattern it produces must ultimately have a simple repetitive
or nested form.
If two elements are removed at each step, however, then this is no longer true. And indeed, as the
pictures on the next page demonstrate, the behavior that is obtained in this case can often be very
complicated.


Examples of tag systems in which a single element is removed from the beginning of the sequence at each step,
and a new block of elements is added to the end of the sequence according to the rules shown. Because only a
single element is removed at each step, the systems effectively just cycle through all elements, replacing each one
in turn. And after every complete cycle, the sequences obtained correspond exactly to the sequences produced on
successive steps in the first three ordinary neighbor-independent substitution systems shown on page 83.

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Examples of tag systems in which at each step two elements are removed from the beginning of the sequence and
then, based on what these elements are, a specified block of new elements is added to the end of the sequence.
(The three dots in the representation of each rule stand for the rest of the elements in the sequence.) The pictures
at the top show the first hundred steps in evolution according to various rules starting from a pair of black
elements. The plots show the total lengths of the sequences obtained in each case. Note that in case (c), all the
elements are eventually removed from the sequence.

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Cyclic Tag Systems

The basic operation of the tag systems that we discussed in the previous section is extremely
simple. But it turns out that by using a slightly different setup one can construct systems whose
operation is in some ways even simpler. In an ordinary tag system, one does not know in advance
which of several possible blocks will be added at each step. But the idea of a cyclic tag system is to
make the underlying rule already specify exactly what block can be added at each step.
In the simplest case there are two possible blocks, and the rule simply alternates on successive
steps between these blocks, adding a block at a particular step when the first element in the sequence
at that step is black. The picture below shows an example of how this works.
The next page shows examples of several cyclic tag systems. In cases (a) and (b) simple behavior
is obtained. In case (c) the behavior is slightly more complicated, but if the pattern is viewed in the
appropriate way then it turns out to have the same nested form as the third neighbor-independent
substitution system shown on page 83.
So what about cases (d) and (e)? In both of these, the sequences obtained at successive steps grow
on average progressively longer. But if one looks at the fluctuations in this growth, as in the plots on
the next page, then one finds that these fluctuations are in many respects random.

An example of a cyclic tag system. There are two cases in the rule, and these cases are used on alternate steps,
as indicated by the circle icons on the left. In each case a single element is removed from the beginning of the
sequence, and then a new block is added at the end whenever the element removed is black. The rule can be
summarized just by giving the blocks to be used in each case, as shown below.

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Examples of cyclic tag systems. In each case the initial condition consists of a single black element. In case (c),
alternate steps in the leftmost column (which in all cyclic tag systems determines the overall behavior) have the
same nested form as the third neighbor-independent substitution system shown on page 83.

Fluctuations in the growth of sequences for cyclic tag systems (d) and (e) above. The fluctuations are shown with
respect to growth at an average rate of half an element per step.

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Register Machines

All of the various kinds of systems that we have discussed so far in this chapter can readily be
implemented on practical computers. But none of them at an underlying level actually work very
much like typical computers. Register machines are however specifically designed to be very simple
idealizations of present-day computers.
Under most everyday circumstances, the hardware construction of the computers we use is hidden
from us by many layers of software. But at the lowest level, the CPUs of all standard computers have
registers that store numbers, and any program we write is ultimately converted into a sequence of
simple instructions that specify operations to be performed on these registers.
Most practical computers have quite a few registers, and support perhaps tens of different kinds of
instructions. But as a simple idealization one can consider register machines with just two registers--
each storing a number of any size--and just two kinds of instructions: "increments" and "decrement-
jumps". The rules for such register machines are then idealizations of practical programs, and are
taken to consist of fixed sequences of instructions, to be executed in turn.
Increment instructions are set up just to increase by one the number stored in a particular register.
Decrement-jump instructions, on the other hand, do two things. First, they decrease by one the number
in a particular register. But then, instead of just going on to execute the next instruction in the
program, they jump to some specified other point in the program, and begin executing again from
there.
Since we assume that the numbers in our registers cannot be negative, however, a register that is
already zero cannot be decremented. And decrement-jump instructions are then set up so that if they
are applied to a register containing zero, they just do essentially nothing: they leave the register
unchanged, and then they go on to execute the next instruction in the program, without jumping
anywhere.

This feature of decrement-jump instructions may seem like a detail, but in fact it is crucial--for it is
what makes it possible for our register machines to take different paths depending on values in
registers through the programs they are given.

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And with this setup, the pictures above show three very simple examples of register machines with
two registers. The programs for each of the machines are given at the top, with ► representing an
increment instruction, and ◄ a decrement-jump. The successive steps in the evolution of each
machine are shown on successive lines down the page. The instruction being executed is indicated at
each step by the position of the dot on the left, while the numbers in each of the two registers are
indicated by the gray blocks on the right.
All the register machines shown start by executing the first instruction in their programs. And with
the particular programs used here, the machines are then set up just to execute all the other
instructions in their programs in turn, jumping back to the beginning of their programs whenever they
reach the end.
Both registers in each machine are initially zero. And in the first machine, the first register
alternates between 0 and 1, while the second remains zero. In the second machine, however, the first
register again

Examples of simple register machines, set up to mimic the low-level operation of practical computers. The
machines shown have two registers, whose values on successive steps are given on successive lines down the
page. Each machine follows a fixed program given at the top. The program consists of a sequence of increment ►
and decrement-jump ◄ instructions. Instructions that are shown as light gray boxes refer to the first register; those
shown as dark gray boxes refer to the second one. On each line going down the page, the black dot on the left
indicates which instruction in the program is being executed at the corresponding step. With the particular
programs shown here, each machine just executes successive instructions in turn, jumping to the beginning again
when it reaches the end of the program.

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alternates between 0 and 1, but the second register progressively grows. And finally, in the third
machine both registers grow.
But in all these three examples, the overall behavior is essentially repetitive. And indeed it turns
out that among the 10,552 possible register machines with programs that are four or fewer instructions
long, not a single one exhibits more complicated behavior.
However, with five instructions, slightly more complicated behavior becomes possible, as the
picture below shows. But even in this example, there is still a highly regular nested structure.
And it turns out that even with up to seven instructions, none of the 276,224,376 programs that are
possible lead to substantially more complicated behavior. But with eight instructions, 126 out of the
11,019,960,576 possible programs finally do show more complicated behavior. The next page gives
an example.

A register machine that shows nested rather than strictly repetitive behavior. The register machine has a program
which is five instructions long. It turns out that this program is one of only two (which differ just by interchange of
the first and second registers) out of the 248,832 possible programs with five instructions that yield anything other
than strictly repetitive behavior.

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Looking just at the ordinary evolution labelled (a), however, the system might still appear to have
quite simple and regular behavior. But a closer examination turns out to reveal irregularities. Part (b)
of the picture shows a version of the evolution compressed to include only

A register machine whose behavior seems in some ways random. The program for this register machine is eight
instructions long. Testing all 11,019,960,576 possible programs of length eight revealed just this and 125 similar
cases of complex behavior. Part (b) shows the evolution in compressed form, with only those steps included at
which either of the registers has just decreased to zero. The values of the nonzero registers are shown using a
logarithmic scale. Part (c) shows the instructions that are executed for the first 400 times that one of the registers is
decreased to zero. Finally, part (d) gives the successive values attained by the second register at steps where the
first register has just decreased to zero. These values are given here as binary digit sequences. As discussed on
page 122, the values can in fact be obtained by a simple arithmetic rule, without explicitly following each step in the
evolution of the register machine. If one value is n, then the next value is 3n/2 if n is even, and (3n+1)/2 if n is
odd. The initial condition is n=1.

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those steps at which one of the two registers has just decreased to zero. And in this picture one
immediately sees some apparently random variation in the instructions that are executed.
Part (c) of the picture then shows which instructions are executed for the first 400 times one of the
registers has just decreased to zero. And part (d) finally shows the base 2 digits of the successive
values attained by the second register when the first register has just decreased to zero. The results
appear to show considerable randomness.
So even though it may not be as obvious as in some of the other systems we have studied, the
simple register machine on the facing page can still generate complex and seemingly quite random
behavior.
So what about more complicated register machines?
An obvious possibility is to allow more than two registers. But it turns out that very little is
normally gained by doing this. With three registers, for example, seemingly random behavior can be
obtained with a program that is seven rather than eight instructions long. But the actual behavior of the
program is almost indistinguishable from what we have already seen with two registers.
Another way to set up more complicated register machines is to extend the kinds of underlying
instructions one allows. One can for example introduce instructions that refer to two registers at a
time, adding, subtracting or comparing their contents. But it turns out that the presence of instructions
like these rarely seems to have much effect on either the form of complex behavior that can occur, or
how common it is.

Yet particularly when such extended instruction sets are used, register machines can provide fairly
accurate idealizations of the low-level operations of real computers. And as a result, programs for
register machines are often very much like programs written in actual low-level computer languages
such as C, Basic, Java or assembler.
In a typical case, each variable in such a program simply corresponds to one of the registers in the
register machine, with no arrays or pointers being allowed. And with this correspondence, our general
results on register machines can also be expected to apply to simple programs written in actual low-
level computer languages

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Practical details make it somewhat difficult to do systematic experiments on such programs. But
the experiments I have carried out do suggest that, just as with simple register machines, searching
through many millions of short programs typically yields at least a few that exhibit complex and
seemingly random behavior.

Symbolic Systems

Register machines provide simple idealizations of typical low-level computer languages. But what
about Mathematica? How can one set up a simple idealization of the transformations on symbolic
expressions that Mathematica does? One approach suggested by the idea of combinators from the
1920s is to consider expressions with forms such as e[e[e][e]][e][e] and then to make
transformations on these by repeatedly applying rules such as e[x_][y_]->x[x[y]], where x_ and y_
stand for any expression.
The picture below shows an example of this. At each step the transformation is done by scanning
once from left to right, and applying the rule wherever possible without overlapping.

A sequence of steps in the evolution of a simple symbolic system. At each step each boxed region is transformed
according to the rule shown. This transformation corresponds to applying the basic Mathematica operation
expression /. rule.

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The structure of expressions like those on the facing page is determined just by their sequence of
opening and closing brackets. And representing these brackets by dark and light squares respectively,
the picture below shows the overall pattern of behavior generated.

More steps in the evolution on the previous page, with opening brackets represented by dark squares and closing
brackets by light ones. In each case configurations wider than the picture are cut off on the right. For the initial
condition from the previous page, the system evolves after 264 steps to a fixed configuration involving 256 opening
brackets followed by 256 closing brackets. For the initial condition on the bottom right, the system again evolves to
a fixed configuration, but now this takes 65,555 steps, and the configuration involves 65,536 opening and closing
brackets. Note that the evolution rules are highly non-local, and are rather unlike those, say, in a cellular

automaton. It turns out that this particular system always evolves to a fixed configuration, but for initial conditions of
size n can take roughly n iterated powers of 2 (or 2^2^2^…) to do so.

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With the particular rule shown, the behavior always eventually stabilizes--though sometimes only
after an astronomically long time.
But it is quite possible to find symbolic systems where this does not happen, as illustrated in the
pictures below. Sometimes the behavior that is generated in such systems has a simple repetitive or
nested form. But often--just as in so many other kinds of systems--the behavior is instead complex and
seemingly quite random.

The behavior of various symbolic systems starting from the initial condition e[e[e][e]][e][e]. The plots at the
bottom show the difference in size of the expressions obtained on successive steps

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Some Conclusions

In the chapter before this one, we discovered the remarkable fact that even though their underlying
rules are extremely simple, certain cellular automata can nevertheless produce behavior of great
complexity.
Yet at first, this seems so surprising and so outside our normal experience that we may tend to
assume that it must be a consequence of some rare and special feature of cellular automata, and must
not occur in other kinds of systems.
For it is certainly true that cellular automata have many special features. All their elements, for
example, are always arranged in a rigid array, and are always updated in parallel at each step. And one
might think that features like these could be crucial in making it possible to produce complex behavior
from simple underlying rules.
But from our study of substitution systems earlier in this chapter we know, for example, that in
fact it is not necessary to have elements that are arranged in a rigid array. And from studying mobile
automata, we know that updating in parallel is also not critical.
Indeed, I specifically chose the sequence of systems in this chapter to see what would happen
when each of the various special features of cellular automata were taken away. And the remarkable
conclusion is that in the end none of these features actually matter much at all. For every single type
of system in this chapter has ultimately proved capable of producing very much the same kind of
complexity that we saw in cellular automata.
So this suggests that in fact the phenomenon of complexity is quite universal--and quite
independent of the details of particular systems.
But when in general does complexity occur?
The examples in this chapter suggest that if the rules for a particular system are sufficiently simple,
then the system will only ever exhibit purely repetitive behavior. If the rules are slightly more

complicated, then nesting will also often appear. But to get complexity in the overall behavior of a
system one needs to go beyond some threshold in the complexity of its underlying rules

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The remarkable discovery that we have made, however, is that this threshold is typically extremely
low. And indeed in the course of this chapter we have seen that in every single one of the general
kinds of systems that we have discussed, it ultimately takes only very simple rules to produce behavior
of great complexity.
One might nevertheless have thought that if one were to increase the complexity of the rules, then
the behavior one would get would also become correspondingly more complex. But as the pictures on
the facing page illustrate, this is not typically what happens.
Instead, once the threshold for complex behavior has been reached, what one usually finds is that
adding complexity to the underlying rules does not lead to any perceptible increase at all in the overall
complexity of the behavior that is produced.
The crucial ingredients that are needed for complex behavior are, it seems, already present in
systems with very simple rules, and as a result, nothing fundamentally new typically happens when
the rules are made more complex. Indeed, as the picture on the facing page demonstrates, there is
often no clear correlation between the complexity of rules and the complexity of behavior they
produce. And this means, for example, that even with highly complex rules, very simple behavior still
often occurs.
One observation that can be made from the examples in this chapter is that when the behavior of a
system does not look complex, it tends to be dominated by either repetition or nesting. And indeed, it
seems that the basic themes of repetition, nesting, randomness and localized structures that we already
saw in specific cellular automata in the previous chapter are actually very general, and in fact
represent the dominant themes in the behavior of a vast range of different systems.
The details of the underlying rules for a specific system can certainly affect the details of the
behavior it produces. But what we have seen in this chapter is that at an overall level the typical types
of behavior that occur are quite universal, and are almost completely independent of the details of
underlying rules.
And this fact has been crucial in my efforts to develop a coherent science of the kind I describe in
this book. For it is what implies that

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Examples of cellular automata with rules of varying complexity. The rules used are of the so-called totalistic type
described on page 60. With two possible colors, just 4 cases need to be specified in such rules, and there are 16
possible rules in all. But as the number of colors increases, the rules rapidly become more complex. With three
colors, there are 7 cases to be specified, and 2187 possible rules; with five colors, there are 13 cases to be

specified, and 1,220,703,125 possible rules. But even though the underlying rules increase rapidly in complexity,
the overall forms of behavior that we see do not change much. With two colors, it turns out that no totalistic rules
yield anything other than repetitive or nested behavior. But as soon as three colors are allowed, much more
complex behavior is immediately possible. Allowing four or more colors, however, does not further increase the
complexity of the behavior, and, as the picture shows, even with five colors, simple repetitive and nested behavior
can still occur

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there are general principles that govern the behavior of a wide range of systems, independent of the
precise details of each system.
And it is this that means that even if we do not know all the details of what is inside some specific
system in nature, we can still potentially make fundamental statements about its overall behavior.
Indeed, in most cases, the important features of this behavior will actually turn out to be ones that we
have already seen with the various kinds of very simple rules that we have discussed in this chapter.

How the Discoveries in This Chapter Were Made

This chapter--and the last--have described a series of surprising discoveries that I have made about
what simple programs typically do. And in making these discoveries I have ended up developing a
somewhat new methodology--that I expect will be central to almost any fundamental investigation in
the new kind of science that I describe in this book.
Traditional mathematics and the existing theoretical sciences would have suggested using a basic
methodology in which one starts from whatever behavior one wants to study, then tries to construct
examples that show this behavior. But I am sure that had I used this approach, I would not have got
very far. For I would have looked only for types of behavior that I already believed might exist. And
in studying cellular automata, this would for example probably have meant that I would only have
looked for repetition and nesting.
But what allowed me to discover much more was that I used instead a methodology fundamentally
based on doing computer experiments.
In a traditional scientific experiment, one sets up a system in nature and then watches to see how it
behaves. And in much the same way, one can set up a program on a computer and then watch how it
behaves. And the great advantage of such an experimental approach is that it does not require one to
know in advance exactly what kinds of behavior can occur. And this is what makes it possible to
discover genuinely new phenomena that one did not expect.
Experience in the traditional experimental sciences might suggest, however, that experiments are
somehow always fundamentally imprecise.

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For when one deals with systems in nature it is normally impossible to set up or measure them
with perfect precision--and indeed it can be a challenge even to make a traditional experiment be at all
repeatable.

But for the kinds of computer experiments I do in this book, there is no such issue. For in almost
all cases they involve programs whose rules and initial conditions can be specified with perfect
precision--so that they work exactly the same whenever and wherever they are run.
In many ways these kinds of computer experiments thus manage to combine the best of both
theoretical and experimental approaches to science. For their results have the kind of precision and
clarity that one expects of theoretical or mathematical statements. Yet these results can nevertheless be
found purely by making observations.
Yet as with all types of experiments it requires considerable skill and judgement to know how to
set up a computer experiment that will yield meaningful results. And indeed, over the past twenty
years or so my own methodology for doing such experiments has become vastly better.
Over and over again the single most important principle that I have learned is that the best
computer experiments are ones that are as simple and straightforward as possible. And this principle
applies both to the structure of the actual systems one studies--and to the procedures that one uses for
studying them.
At some level the principle of looking at systems with the simplest possible structure can be
viewed as an abstract aesthetic one. But it turns out also to have some very concrete consequences.
For a start, the simpler a structure is, the more likely it is that it will show up in a wide diversity of
different places. And this means that by studying systems with the simplest possible structure one will
tend to get results that have the broadest and most fundamental significance.
In addition, looking at systems with simpler underlying structures gives one a better chance of
being able to tell what is really responsible for any phenomenon one sees--for there are fewer features
that have been put into the system and that could lead one astray.
At a purely practical level, there is also an advantage to studying systems with simpler structures;
for these systems are usually easier to
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implement on a computer, and can thus typically be investigated more extensively with given
computational resources.
But an obvious issue with saying that one should study systems with the simplest possible
structure is that such systems might just not be capable of exhibiting the kinds of behavior that one
might consider interesting--or that actually occurs in nature.
And in fact, intuition from traditional science and mathematics has always tended to suggest that
unless one adds all sorts of complications, most systems will never be able to exhibit any very relevant
behavior. But the results so far in this book have shown that such intuition is far from correct, and that
in reality even systems with extremely simple rules can give rise to behavior of great complexity.
The consequences of this fact for computer experiments are quite profound. For it implies that
there is never an immediate reason to go beyond studying systems with rather simple underlying rules.
But to absorb this point is not an easy matter. And indeed, in my experience the single most common
mistake in doing computer experiments is to look at systems that are vastly more complicated than is
necessary.
Typically the reason this happens is that one just cannot imagine any way in which a simpler
system could exhibit interesting behavior. And so one decides to look at a more complicated system--
usually with features specifically inserted to produce some specific form of behavior.

Much later one may go back and look at the simpler system again. And this is often a humbling
experience, for it is common to find that the system does in fact manage to produce interesting
behavior--but just in a way that one was not imaginative enough to guess.
So having seen this many times I now always try to follow the principle that one can never start
with too simple a system. For at worst, one will just establish a lower limit on what is needed for
interesting behavior to occur. But much more often, one will instead discover behavior that one never
thought was possible.
It should however be emphasized that even in an experiment it is never entirely straightforward to
discover phenomena one did not expect. For in setting up the experiment, one inevitably has to make
assumptions about the kinds of behavior that can occur. And if it turns

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out that there is behavior which does not happen to fit in with these assumptions, then typically the
experiment will fail to notice it.
In my experience, however, the way to have the best chance of discovering new phenomena in a
computer experiment is to make the design of the experiment as simple and direct as possible. It is
usually much better, for example, to do a mindless search of a large number of possible cases than to
do a carefully crafted search of a smaller number. For in narrowing the search one inevitably makes
assumptions, and these assumptions may end up missing the cases of greatest interest.
Along similar lines, I have always found it much better to look explicitly at the actual behavior of
systems, than to work from some kind of summary. For in making a summary one inevitably has to
pick out only certain features, and in doing this one can remove or obscure the most interesting
effects.
But one of the problems with very direct experiments is that they often generate huge amounts of
raw data. Yet what I have typically found is that if one manages to present this data in the form of
pictures then it effectively becomes possible to analyze very quickly just with one's eyes. And indeed,
in my experience it is typically much easier to recognize unexpected phenomena in this way than by
using any kind of automated procedure for data analysis.
It was in a certain sense lucky that one-dimensional cellular automata were the first examples of
simple programs that I investigated. For it so happens that in these systems one can usually get a good
idea of overall behavior just by looking at an array of perhaps 10,000 cells--which can easily be
displayed in few square inches.
And since several of the 256 elementary cellular automaton rules already generate great
complexity, just studying a couple of pages of pictures like the ones at the beginning of this chapter
should in principle have allowed one to discover the basic phenomenon of complexity in cellular
automata.
But in fact I did not make this discovery in such a straightforward way. I had the idea of looking at
pictures of cellular automaton evolution at the very beginning. But the technological difficulty of
producing these pictures made me want to reduce their number as

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much as possible. And so at first I looked only at the 32 rules which had left-right symmetry and made
blank backgrounds stay unchanged.
Among these rules I found examples of repetition and nesting. And with random initial conditions,
I found more complicated behavior. But since I did not expect that any complicated behavior would be
possible with simple initial conditions, I did not try looking at other rules in an attempt to find it.
Nevertheless, as it happens, the first paper that I published about cellular automata--in 1983--did in
fact include a picture of rule 30 from page 27, as an example of a non-symmetric rule. But the picture
showed only 20 steps of evolution, and at the time I did not look carefully at it, and certainly did not
appreciate its significance.
For several years, I did progressively more sophisticated computer experiments on cellular
automata, and in the process I managed to elucidate many of their properties. But finally, when
technology had advanced to the point where it became almost trivial for me to do so, I went back and
generated some straightforward pages of pictures of all 256 elementary rules evolving from simple
initial conditions. And it was upon seeing these pictures that I finally began to appreciate the
remarkable phenomenon that occurs in systems like rule 30.
Seven years later, after I had absorbed some basic intuition from looking at cellular automata like
rule 30, I resolved to find out whether similar phenomena also occurred in other kinds of systems. And
the first such systems that I investigated were mobile automata.
Mobile automata in a sense evolve very slowly relative to cellular automata, so to make more
efficient pictures I came up with a scheme for showing their evolution in compressed form. I then
started off by generating pictures of the first hundred, then the first thousand, then the first ten
thousand, mobile automata. But in all of these pictures I found nothing beyond repetitive and nested
behavior.
Yet being convinced that more complicated behavior must be possible, I decided to persist, and so
I wrote a program that would automatically search through large numbers of mobile automata. I set up
various criteria for the search, based on how I expected mobile automata could behave. And quite
soon, I had made the program search a million mobile automata, then ten million.

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But still I found nothing.
So then I went back and started looking by eye at mobile automata with large numbers of
randomly chosen rules. And after some time what I realized was that with the compression scheme I
was using there could be mobile automata that would be discarded according to my search criteria, but
which nevertheless still had interesting behavior. And within an hour of modifying my search program
to account for this, I found the example shown on page 74.
Yet even after this, there were still many assumptions implicit in my search program. And it took
some time longer to identify and remove them. But having done so, it was then rather straightforward
to find the example shown on page 75.
A somewhat similar pattern has been repeated for most of the other systems described in this
chapter. The main challenge was always to avoid assumptions and set up experiments that were
simple and direct enough that they did not miss important new phenomena.

In many cases it took a large number of iterations to work out the right experiments to do. And had
it not been for the ease with which I could set up new experiments using Mathematica, it is likely that
I would never have gotten very far in investigating most of the systems discussed in this chapter. But
in the end, after running programs for a total of several years of computer time--corresponding to
more than a million billion logical operations--and creating the equivalent of tens of thousands of
pages of pictures, I was finally able to find all of the various examples shown in this chapter and the
ones that follow.

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Systems Based on Numbers
The Notion of Numbers

Much of science has in the past ultimately been concerned with trying to find ways to describe
natural systems in terms of numbers.
Yet so far in this book I have said almost nothing about numbers. The purpose of this chapter,
however, is to investigate a range of systems that are based on numbers, and to see how their behavior
compares with what we have found in other kinds of systems.
The main reason that systems based on numbers have been so popular in traditional science is that
so much mathematics has been developed for dealing with them. Indeed, there are certain kinds of
systems based on numbers whose behavior has been analyzed almost completely using mathematical
methods such as calculus.
Inevitably, however, when such complete analysis is possible, the final behavior that is found is
fairly simple.
So can systems that are based on numbers ever in fact yield complex behavior? Looking at most
textbooks of science and mathematics, one might well conclude that they cannot. But what one must
realize is that the systems discussed in these textbooks are usually ones that are specifically chosen to
be amenable to fairly complete analysis, and whose behavior is therefore necessarily quite simple.
And indeed, as we shall see in this chapter, if one ignores the need for analysis and instead just
looks at the results of computer

(ini hal. 115 karena hal 114 nya ga ada)

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experiments, then one quickly finds that even rather simple systems based on numbers can lead to
highly complex behavior.
But what is the origin of this complexity? And how does it relate to the complexity we have seen
in systems like cellular automata?

One might think that with all the mathematics developed for studying systems based on numbers it
would be easy to answer these kinds of questions. But in fact traditional mathematics seems for the
most part to lead to more confusion than help.
One basic problem is that numbers are handled very differently in traditional mathematics from the
way they are handled in computers and computer programs. For in a sense, traditional mathematics
makes a fundamental idealization: it assumes that numbers are elementary objects whose only relevant
attribute is their size. But in a computer, numbers are not elementary objects. Instead, they must be
represented explicitly, typically by giving a sequence of digits.
The idea of representing a number by a sequence of digits is familiar from everyday life: indeed,
our standard way of writing numbers corresponds exactly to giving their digit sequences in base 10.
What base 10 means is that for each digit there are 10 possible choices:

Representations of the number 3829 in various bases. The most familiar case is base 10, where starting from the
right successive digits correspond to units, tens, hundreds and so on. In base 10, there are 10 possible digits: 0
through 9. In other bases, there are a different number of possible digits. In base 2, as used in practical computers,
there are just two possible digits: 0 and 1. And in this base, successive digits starting from the right have
coefficients 1, 2, 4 = 2×2, 8 = 2×2×2, etc.

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0 through 9. But as the picture at the bottom of the facing page shows, one can equally well use
other bases. And in practical computers, for example, base 2 is almost always what is used.
So what this means is that in a computer numbers are represented by sequences of 0's and 1's,
much like sequences of white and black cells in systems like cellular automata. And operations on
numbers then correspond to ways of updating sequences of 0's and 1's.
In traditional mathematics, the details of how operations performed on numbers affect sequences
of digits are usually considered quite irrelevant. But what we will find in this chapter is that precisely
by looking at such details, we will be able to see more clearly how complexity develops in systems
based on numbers.
In many cases, the behavior we find looks remarkably similar to what we saw in the previous
chapter. Indeed, in the end, despite some confusing suggestions from traditional mathematics, we will
discover that the general behavior of systems based on numbers is very similar to the general behavior
of simple programs that we have already discussed.

Elementary Arithmetic

The operations of elementary arithmetic are so simple that it seems impossible that they could ever
lead to behavior of any great complexity. But what we will find in this section is that in fact they can.
To begin, consider what is perhaps the simplest conceivable arithmetic process: start with the
number 1 and then just progressively add 1 at each of a sequence of steps.
The result of this process is to generate the successive numbers 1, 2, 3, 4, 5, 6, 7, 8, ... The sizes of
these numbers obviously form a very simple progression.

But if one looks not at these overall sizes, but rather at digit sequences, then what one sees is
considerably more complicated. And in fact, as the picture on the right demonstrates, these successive
digit sequences form a pattern that shows an intricate nested structure.

Digit sequences of successive numbers written in base 2. The overall pattern has an intricate nested form.

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The pictures below show what happens if one adds a number other than 1 at each step. Near the
right-hand edge, each pattern is somewhat different. But at an overall level, all the patterns have
exactly the same basic nested structure.
If instead of addition one uses multiplication, however, then the results one gets can be very
different. The first picture at the top of the facing page shows what happens if one starts with 1 and
then successively multiplies by 2 at each step.
It turns out that if one represents numbers as digit sequences in base 2, then the operation of
multiplying by 2 has a very simple effect: it just shifts the digit sequence one place to the left, adding a
0 digit on the right. And as a result, the overall pattern obtained by successive multiplication by 2 has
a very simple form.

Digit sequences in base 2 of numbers obtained by starting with 1 and then successively adding a constant at each
step. All these patterns ultimately have the same overall nested form.

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But if the multiplication factor at each step is 3, rather than 2, then the pattern obtained is quite
different, as the second picture above shows. Indeed, even though the only operation used was just
simple multiplication, the final pattern obtained in this case is highly complex.
The picture on the next page shows more steps in the evolution of the system. At a small scale,
there are some obvious triangular and other structures, but beyond these the pattern looks essentially
random.
So just as in simple programs like cellular automata, it seems that simple systems based on
numbers can also yield behavior that is highly complex and apparently random.
But we might imagine that the complexity we see in pictures like the one on the next page must
somehow be a consequence of the fact that we are looking at numbers in terms of their digit
sequences--and would not occur if we just looked at numbers in terms of their overall size.
A few examples, however, will show that this is not the case.
To begin the first example, consider what happens if one multiplies by 3/2, or 1.5, at each step.
Starting with 1, the successive numbers that one obtains in this way are 1, 3/2 = 1.5, 9/4 = 2.25,
27/8 = 3.375, 81/16 = 5.0625, 243/32 = 7.59375, 729/64 =11.390625, ...

Patterns produced by starting with the number 1, and then successively multiplying by a factor of 2, and a factor of
3. In each case, the digit sequence of the number obtained at each step is shown in base 2. Multiplication by 2
turns out to correspond just to shifting all digits in base 2 one position to the left, so that the overall pattern
produced in this case is very simple. But multiplication by 3 yields a much more complicated pattern, as the picture
on the right shows. Note that in these pictures the complete numbers obtained at each step correspond
respectively to the successive integer powers of 2 and of 3.

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The first 500 powers of 3, shown in base 2. Some small-scale structure is visible, but on a larger scale the pattern
seems for all practical purposes random. Note that the pattern shown here has been truncated at the edge of the
page on the left, although in fact the whole pattern continues to expand to the left forever with an average slope of
Log[2, 3] 1.58.

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The picture below shows the digit sequences for these numbers given in base 2. The digits that lie
directly below and to the left of the original 1 at the top of the pattern correspond to the whole number
part of each successive number (e.g. 3 in 3.375), while the digits that lie to the right correspond to the
fractional part (e.g. 0.375 in 3.375).

(ada gambarnya)

And instead of looking explicitly at the complete pattern of digits, one can consider just finding the
size of the fractional part of each successive number. These sizes are plotted at the top of the next
page. And the picture shows that they too exhibit the kind of complexity and apparent randomness that
is evident at the level of digits.

Successive powers of 3/2, shown in base 2. Multiplication by 3/2 can be thought of as multiplication by 3 combined
with division by 2. But division by 2 just does the opposite of multiplication by 2, so in base 2 it simply shifts all
digits one position to the right. The overall pattern is thus a shifted version of the pattern shown on the facing page.

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The example just given involves numbers with fractional parts. But it turns out that similar
phenomena can also be found in systems that involve only whole numbers.
As a first example, consider a slight variation on the operation of multiplying by 3/2 used above:
if the number at a particular step is even (divisible by 2), then simply multiply that number by 3/2,
getting a whole number as the result. But if the number is odd, then first add 1--so as to get an even
number--and only then multiply by 3/2.

Sizes of the fractional parts of successive powers of 3/2. These sizes are completely independent of what base is
used to represent the numbers. Only the dots are significant; the shading and lines between them are just included
to make the plot easier to read.

Results of starting with the number 1, then applying the following rule: if the number at a particular step is even,
multiply by 3/2; otherwise, add 1, then multiply by 3/2. This procedure yields a succession of whole numbers
whose digit sequences in base 2 are shown at the right. The rightmost digits obtained at each step are shown
above. The digit is 0 when the number is even and 1 when it is odd, and, as shown, the digits alternate in a
seemingly random way. It turns out that the system described here is closely related to one that arose in studying
the register machine shown on page 100. The system here can be represented by the rule n->If[EvenQ[n], 3n/
2, 3(n+1)/2], while the one on page 100 follows the rule n->If[EvenQ[n], 3n/2, (3n+1)/2]. After the first
step these systems give the same sequence of numbers, except for an overall factor

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This procedure is always guaranteed to give a whole number. And starting with 1, the sequence of
numbers one gets is 1, 3, 6, 9, 15, 24, 36, 54, 81, 123, 186, 279, 420, 630, 945, 1419, 2130, 3195,
4794, ...
Some of these numbers are even, while some are odd. But as the results at the bottom of the facing
page illustrate, the sequence of which numbers are even and which are odd seems to be completely
random.
Despite this randomness, however, the overall sizes of the numbers obtained still grow in a rather
regular way. But by changing the procedure just slightly, one can get much less regular growth.
As an example, consider the following procedure: if the number obtained at a particular step is
even, then multiply this number by 5/2; otherwise, add 1 and then multiply the result by 1/2.
If one starts with 1, then this procedure simply gives 1 at every step. And indeed with many
starting numbers, the procedure yields purely repetitive behavior. But as the picture below shows, it
can also give more complicated behavior.
Starting for example with the number 6, the sizes of the numbers obtained on successive steps
show a generally increasing trend, but there are considerable fluctuations, and these fluctuations seem
to be essentially random. Indeed, even after a million steps, when the

Results of applying the rule n->If[EvenQ[n], 5n/2, (n+1)/2], starting with different initial choices of n. In many
cases, the behavior obtained is purely repetitive. But in some cases it is not.

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number obtained has 48,554 (base 10) digits, there is still no sign of repetition or of any other
significant regularity.
So even if one just looks at overall sizes of whole numbers it is still possible to get great
complexity in systems based on numbers.
But while complexity is visible at this level, it is usually necessary to go to a more detailed level in
order to get any real idea of why it occurs. And indeed what we have found in this section is that if
one looks at digit sequences, then one sees complex patterns that are remarkably similar to those
produced by systems like cellular automata.
The underlying rules for systems like cellular automata are however usually rather different from
those for systems based on numbers. The main point is that the rules for cellular automata are always
local: the new color of any particular cell depends only on the previous color of that cell and its
immediate neighbors. But in systems based on numbers there is usually no such locality.
One knows from hand calculation that even an operation such as addition can lead to "carry" digits
which propagate arbitrarily far to the left. And in fact most simple arithmetic operations have the
property

The results of following the same rule as on the previous page, starting from the value 6. Plotted on the right are
the overall sizes of the numbers obtained for the first thousand steps. The plot is on a logarithmic scale, so the
height of each point is essentially the length of the digit sequence for the number that it represents--or the width of
the row on the left.

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that a digit which appears at a particular position in their result can depend on digits that were
originally far away from it.
But despite fundamental differences like this in underlying rules, the overall behavior produced by
systems based on numbers is still very similar to what one sees for example in cellular automata.
So just like for the various kinds of programs that we discussed in the previous chapter, the details
of underlying rules again do not seem to have a crucial effect on the kinds of behavior that can occur.
Indeed, despite the lack of locality in their underlying rules, the pictures below and on the pages
that follow [126, 127] show that it is even possible to find systems based on numbers that exhibit
something like the localized structures that we saw in cellular automata on page 32.

An example of a system defined by the following rule: at each step, take the number obtained at that step and write
its base 2 digits in reverse order, then add the resulting number to the original one. For many possible starting
numbers, the behavior obtained is very simple. This picture shows what happens when one starts with the number
16. After 180 steps, it turns out that all that survives are a few objects that one can view as localized structures.

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A thousand steps in the evolution of a system with the same rule as on the previous page, but now starting with the
number 512. Localized structures are visible, but the overall pattern never seems to take on any kind of simple
repetitive form.

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Continuation of the pattern on the facing page, starting at the millionth step. The picture shows the right-hand edge
of the pattern; the complete pattern extends about 700 times the width of the page to the left.

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Recursive Sequences

In the previous section, we saw that it is possible to get behavior of considerable complexity just
by applying a variety of operations based on simple arithmetic. In this section what I will show is that
with the appropriate setup just addition and subtraction turn out to be in a sense the only operations
that one needs.
The basic idea is to consider a sequence of numbers in which there is a definite rule for getting the
next number in the sequence from previous ones. It is convenient to refer to the first number in each
sequence as f[1], the second as f[2], and so on, so that the n th number is denoted f[n]. And with this
^

notation, what the rule does is to specify how f[n] should be calculated from previous numbers in the
sequence.
In the simplest cases, f[n] depends only on the number immediately before it in the sequence,
denoted f[n-1]. But it is also possible to set up rules in which f[n] depends not only on f[n-1], but
also on f[n-2], as well as on numbers still earlier in the sequence.
The table below gives results obtained with a few specific rules. In all the cases shown, these
results are quite simple, consisting of sequences that increase uniformly or fluctuate in a purely
repetitive way.

Examples of some simple recursive sequences. The n th element in each sequence is denoted f[n], and the rule
^

specifies how this element is determined from previous ones. With all the rules shown here, successive elements
either increase smoothly or fluctuate in a purely repetitive way. Sequence (c) is the powers of two; (d) is the so-
called Fibonacci sequence, related to powers of the golden ratio (1+Sqrt[5])/2 1.618. All rules of the kind
shown here lead to sequences where f[n] can be expressed in terms of a simple sum of powers of the form a n. ^

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But it turns out that with slightly more complicated rules it is possible to get much more
complicated behavior. The key idea is to consider rules which look at numbers that are not just a fixed
distance back in the sequence. And what this means is that instead of depending only on quantities
like f[n-1] and f[n-2], the rule for f[n] can also for example depend on a quantity like f[n - f[n-1]].
There is some subtlety here because in the abstract nothing guarantees that n-f[n-1] will
necessarily be a positive number. And if it is not, then results obtained by applying the rule can
involve meaningless quantities such as f[0], f[-1] and f[-2].

Examples of sequences generated by rules that do not depend only on elements a fixed distance back. Most such
rules eventually end up involving meaningless quantities such as f[0] and f[-1], but the particular rules shown
here all avoid this problem.

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Fluctuations in the overall increase of sequences from the previous page. In cases (c) and (d), the fluctuations
have a regular nested form, and turn out to be directly related to the base 2 digit sequence of n. In the other cases,
the fluctuations are more complicated, and seem in many respects random. All the rules shown start with
f[1]=f[2]=1.

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